Randomized Lasso works by resampling the train data and computing
a Lasso on each resampling. In short, the features selected more
often are good features. It is also known as stability selection.

Parameters:

alpha : float, ‘aic’, or ‘bic’, optional

The regularization parameter alpha parameter in the Lasso.
Warning: this is not the alpha parameter in the stability selection
article which is scaling.

scaling : float, optional

The alpha parameter in the stability selection article used to
randomly scale the features. Should be between 0 and 1.

sample_fraction : float, optional

The fraction of samples to be used in each randomized design.
Should be between 0 and 1. If 1, all samples are used.

n_resampling : int, optional

Number of randomized models.

selection_threshold: float, optional :

The score above which features should be selected.

fit_intercept : boolean, optional

whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(e.g. data is expected to be already centered).

verbose : boolean or integer, optional

Sets the verbosity amount

normalize : boolean, optional, default True

If True, the regressors X will be normalized before regression.

precompute : True | False | ‘auto’

Whether to use a precomputed Gram matrix to speed up
calculations. If set to ‘auto’ let us decide. The Gram
matrix can also be passed as argument.

max_iter : integer, optional

Maximum number of iterations to perform in the Lars algorithm.

eps : float, optional

The machine-precision regularization in the computation of the
Cholesky diagonal factors. Increase this for very ill-conditioned
systems. Unlike the ‘tol’ parameter in some iterative
optimization-based algorithms, this parameter does not control
the tolerance of the optimization.

n_jobs : integer, optional

Number of CPUs to use during the resampling. If ‘-1’, use
all the CPUs

If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by np.random.

pre_dispatch : int, or string, optional

Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:

None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs

An int, giving the exact number of total jobs that are
spawned

A string, giving an expression as a function of n_jobs,
as in ‘2*n_jobs’

memory : Instance of joblib.Memory or string

Used for internal caching. By default, no caching is done.
If a string is given, it is the path to the caching directory.

Attributes:

scores_ : array, shape = [n_features]

Feature scores between 0 and 1.

all_scores_ : array, shape = [n_features, n_reg_parameter]

Feature scores between 0 and 1 for all values of the regularization parameter. The reference article suggests scores_ is the max of all_scores_.

The method works on simple estimators as well as on nested objects
(such as pipelines). The former have parameters of the form
<component>__<parameter> so that it’s possible to update each
component of a nested object.